SeaD: End-to-end Text-to-SQL Generation with Schema-aware Denoising (2105.07911v2)

Abstract: In text-to-SQL task, seq-to-seq models often lead to sub-optimal performance due to limitations in their architecture. In this paper, we present a simple yet effective approach that adapts transformer-based seq-to-seq model to robust text-to-SQL generation. Instead of inducing constraint to decoder or reformat the task as slot-filling, we propose to train seq-to-seq model with Schema aware Denoising (SeaD), which consists of two denoising objectives that train model to either recover input or predict output from two novel erosion and shuffle noises. These denoising objectives acts as the auxiliary tasks for better modeling the structural data in S2S generation. In addition, we improve and propose a clause-sensitive execution guided (EG) decoding strategy to overcome the limitation of EG decoding for generative model. The experiments show that the proposed method improves the performance of seq-to-seq model in both schema linking and grammar correctness and establishes new state-of-the-art on WikiSQL benchmark. The results indicate that the capacity of vanilla seq-to-seq architecture for text-to-SQL may have been under-estimated.

• The paper presents SeaD, an approach that employs schema-aware denoising with erosion and shuffle to improve schema linking in text-to-SQL tasks.
• It leverages a Transformer-based architecture with clause-sensitive execution guided decoding to boost logical (84.7%) and execution (90.1%) accuracy on WikiSQL.
• SeaD demonstrates that integrating task-oriented denoising can simplify sequence-to-sequence learning and broaden the utility of text-to-SQL generation methods.

SeaD: End-to-end Text-to-SQL Generation with Schema-aware Denoising

The paper "SeaD: End-to-end Text-to-SQL Generation with Schema-aware Denoising" focuses on enhancing the performance of seq-to-seq (S2S) models in the text-to-SQL task by addressing schema linking and grammar issues. This task translates natural language questions into SQL queries, making it useful for non-technical users to interact with structured databases.

Key Contributions

The authors present several innovations to improve S2S models:

1. Schema-aware Denoising (SeaD): Introduces two denoising objectives, erosion and shuffle. These objectives enrich the model's understanding by reconstructing inputs from corrupted data, enhancing schema linking and syntactic correctness.
2. Clause-sensitive Execution Guided (EG) Decoding: Enhances the traditional EG decoding method by dynamically adjusting beam sizes, improving generation accuracy.

Schema-aware Denoising Objectives

• Erosion: Alters the schema by scrambling, deleting, or adding columns, requiring the model to adapt its SQL generation based on changes. This promotes the model’s ability to identify correct schema entities.
• Shuffle: Reorders entities within text or SQL, helping the model learn the relationships among entities and improve schema linking.

Methodology

The paper leverages a Transformer-based architecture combined with a Hybrid Pointer Generator Network. This architecture employs schema-aware denoising objectives, augmenting the model's abilities without relying on slot-filling methods or structural constraints.

Experimental Results

The model was tested on the WikiSQL dataset, resulting in state-of-the-art performance. Key metrics such as logical form accuracy ($Acc_{lf}$) and execution accuracy ($Acc_{ex}$) demonstrated significant improvements over existing models, with SeaD achieving 84.7% $Acc_{lf}$ and 90.1% $Acc_{ex}$ on the test set.

Implications and Future Directions

The paper suggests that the inherent capacity of vanilla S2S models might have been undervalued. SeaD’s improvements imply potential broader applicability of task-oriented denoising for various S2S tasks. Future work could explore incorporating these objectives in different dataset domains or expanding this approach to more intricate SQL queries.

In conclusion, SeaD represents a substantive advancement in text-to-SQL generation by integrating schema-aware denoising techniques, reducing dependence on complex structural interventions while maintaining model simplicity. This work suggests promising avenues for further AI developments in human-database interaction tasks.